今村 有佑

OBJECTIVES: To evaluate the success rate of shock wave lithotripsy and identify predictors of stone-free status after shock wave lithotripsy for ureteral stones, focusing on the impact of stones remaining in the same location for 2 months (SSL2). METHODS: A retrospective analysis was conducted on 501 patients with ureteral stones treated with shock wave lithotripsy by expert surgeons (each with over 1000 shock wave lithotripsy operations) at a single Japanese institution in 2020. Logistic regression analysis identified predictors of stone-free status, including stone length, skin-to-stone distance, stone density (Hounsfield Unit), Hounsfield Unit above/below the stone, stone position, and duration of stone at the same location (SSL2). RESULTS: Ninety patients were excluded, resulting in 411 patients undergoing an average of 1.15 ± 0.4 sessions (range: 1-4). 344 patients (83.7%) achieved stone-free status after a single session. The overall 1-month stone-free rate was 71.4%, and the 3-month stone-free rate was 88.8%. Stone at the same location ≥2 months (SSL2) was an independent predictor of 1-month stone-free status (odds ratio = 2.25, 95%CI: 1.10-4.57, p = 0.025), while mean stone density ≥ 813 HU was an independent predictor of 3-month stone-free status (odds ratio = 2.66, 95% CI: 1.10-6.45, p = 0.03). CONCLUSION: Stone at the same location ≥2 months (SSL2) was a potent predictor of 1-month and 3-month stone-free status. This condition is associated with impacted stones and can aid in decision-making for shock wave lithotripsy treatment selection.

BACKGROUND: This study investigated the characteristics of prostate-specific antigen (PSA) dynamics when androgen receptor signaling inhibitor (ARSI), or vintage agent (bicalutamide) was used for patients with metastatic hormone-sensitive prostate cancer (mHSPC). PATIENTS AND METHODS: A total of 213 mHSPC patients from each of the ARSI and bicalutamide groups treated between 2015 and 2022 were selected from multiple institutions using propensity score-matched analysis to align backgrounds. PSA progression-free survival (PFS) and overall survival (OS) were assessed. PSA level at 3 months, PSA nadir level, and time to PSA nadir were examined to analyze of PSA kinetics. RESULTS: ARSI treatment significantly improved PSA PFS compared to bicalutamide (P = 0.0063), although no significant difference in OS was seen (P = 0.3134). No significant differences were observed between treatment groups in median PSA levels at 3 months (1.47 vs 0.52 ng/ml, P = 0.3042) or PSA nadir levels (0.263 vs 0.1345 ng/ml, P = 0.1228). Bicalutamide treatment demonstrated longer time to nadir than ARSI in progression-free cases (median: 243 vs 213.5 days, P = 0.0003). Survival tree analysis found that PSA nadir ≤ 1.5 ng/ml and time to nadir ≥ 145 days were the optimal cut-offs for best stratifying OS with bicalutamide, while PSA nadir ≤ 0.45 ng/ml and time to nadir ≥ 70 days were optimal with ARSI. CONCLUSION: No significant differences in PSA response was seen between groups; however, distinct optimal cut-offs were demonstrated for PSA nadir and time to nadir. The present findings will be useful for optimal PSA monitoring for mHSPC patients and for early identification of poor-prognosis populations.

BACKGROUND: For biochemical recurrence following radical prostatectomy for prostate cancer, treatments such as radiation therapy and androgen deprivation therapy are administered. To diagnose postoperative recurrence as early as possible and to intervene with treatment at the appropriate time, it is essential to accurately predict recurrence after radical prostatectomy. However, postoperative recurrence involves numerous patient-related factors, making its prediction challenging. The purpose of this study is to accurately predict the timing of biochemical recurrence after radical prostatectomy and to analyze the risk factors for follow-up of high-risk patients and early detection of recurrence. METHODS: We utilized the machine learning survival analysis model called the Random Survival Forest utilizing the 58 clinical factors from 548 patients who underwent radical prostatectomy at Chiba University Hospital. To visualize prognostic factors and assess accuracy of the time course probability, we employed SurvSHAP(t) and time-dependent Area Under Cureve(AUC). RESULTS: The time-dependent AUC of RSF was 0.785, which outperformed the Cox proportional hazards model (0.704), the Cancer of the Prostate Risk Assessment (CAPRA) score (0.710), and the D'Amico score (0.658). The key prognostic factors for early recurrence were Gleason score(GS), Seminal vesicle invasion(SV), and PSA. The contribution of PSA to recurrence decreases after the first year, while SV and GS increase over time. CONCLUSION: Our prognostic model analyzed the time-dependent relationship between the timing of recurrence and prognostic factors. Our study achieved personalized prognosis analysis and its rationale after radical prostatectomy by employing machine learning prognostic model. This prognostic model contributes to the early detection of recurrence by enabling clinicians to conduct appropriate follow-ups for high-risk patients.

OBJECTIVES: Renal cell carcinoma (RCC) is shown to have a tendency for late recurrence, occurring 5 or more years after curative surgery. Imaging diagnosis is required for follow-up, and there is no definitive answer as to how long this should continue. Some patients discontinue follow-up visits at their own discretion. How best to predict late recurrence and loss to follow-up (LF) remains unclear. PATIENTS AND METHODS: This study targeted patients diagnosed with non-metastatic RCC who underwent either radical or partial nephrectomy at Chiba University Hospital between 1988 and 2021. Follow-up for patients with RCC is typically lifelong. We used random survival forests (RSFs), a machine learning-based survival analysis method, to predict late recurrence and LF. For verification of prediction accuracy, we applied the time-dependent area under the receiver operating characteristic curve (t-AUC). To analyse the risks of late recurrence and LF, SurvSHAP(t) and partial dependence plots were used. RESULTS: We analysed 1051 cases in this study. Median follow-up was 58.5 (range: 0-376) months. The predictive accuracy of recurrence using RSF was t-AUC 0.806, 0.761, 0.674 and 0.566 at 60, 120, 180 and 240 months postoperatively, respectively. The recurrence risk impact showed a time-dependent increase up to approximately 50 months postoperatively. Beyond 50 months, there were no distinct risk factors characteristic of late recurrence. The predictive accuracy of LF using RSF was t-AUC 0.542, 0.699, 0.685, 0.628 and 0.674 at 60, 120, 180, 240 and 300 months postoperatively, respectively. The risk of LF increased with advancing age beyond 70 years. CONCLUSION: It is difficult to identify factors that predict late recurrence. For long-term follow-up observation, it is essential to pay particular attention to patients with RCC aged 70 years and above. Establishing frameworks to facilitate collaboration with local hospitals near patients' residences and providing care within the community is necessary.